Data-free diffusion training matches data-based results in lattice field theory
A new training approach uses the system's own action to guide learning, not a pre-made data set, in two-dimensional lattice field theory.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Dennis Grinwald, Ankur Singha, Khaled Kahouli, Karl Jansen, Shinichi Nakajima
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
Researchers trained diffusion-based samplers without target data, guiding learning with the system action. The data-free approach produced results similar to models trained with a common simulation method across several lattice sizes.
Why it matters
If proven broadly, this could cut the time and cost of making training data for complex physics simulations while keeping accuracy.
The method uses the lattice action and its slope instead of real data. In a simple scalar theory, results stayed similar to data-driven ones.
A practical note: reducing sample creation lowers work, but careful tuning and validation are still needed to keep results trustworthy in physics problems overall.
What remains uncertain
Results come from a two-dimensional scalar theory; applicability to larger, more complex systems is not yet shown.
Original sources · 1
- Data Free Training of Diffusion Models for Lattice Field Theories ↗arXiv · 2026-10-13
Check the original paper for its authors, methods, version and access terms.
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